This project will pursue statistical methods in several main areas of AIDS research. The sense of urgency surrounding the AIDS crisis has led to the need for methods enabling more effective and efficient design and analysis of clinical trials. To reduce the time and resources required to conduct studies, multivariate extensions of group sequential designs will be developed using counting process tools, and the role surrogates for long term clinical efficacy measures will be investigated. New robust nonparametric methods for analysis of mismeasured or missing covariate data will be explored. Estimation of HIV prevalence and projection of AIDS epidemic parameters improves the understanding of the dynamics of the epidemic and provides a basis for predicting its future course for public health planning purposes. Precise estimates of prevalence will be obtained by developing statistical methods for assays performed on pooled samples, through use of Polymerase Chain Reaction Assays, and by using small area estimation techniques. Novel statistical methods based on a pseudo-likelihood function will be developed for the estimation of various parameters of the AIDS epidemic, when one uses HIV prevalence data augmented by parameter estimates from other sources of data. All proposed methods will be implemented in actual AIDS research data sets. The clinical and psychological course for ARc and AIDS patients entering the health care system can be quite complex. Flexible statistical methodology will be explored which will allow a better understanding of this course through the analysis of data arising in registries and in clinical trails. Of particular interest will be multivariate point process models which allow time dependent covariates. There are particularly compelling reasons to obtain approaches to insuring security of confidential AIDS research and patient care data. Two probabilistic data security approaches will be developed, based on hash code identifier methods and partial information file matching methods.